Polygenic Scores Clarify the Relationship Between Mental Health and Gender Diversity

Rapidly evolved genomic regions shape individual language abilities in present-day humans

1Minor genetic changes have produced profound differences in cognitive abilities between humans and our closest relatives, particularly in language. Despite decades of research, ranging from single-gene studies to broader evolutionary analyses[1, 2, 3, 4, 5], key questions about the genomic foundations of human language have persisted, including which sequences are involved, how they evolved, and whether similar changes occur in other vocal learning species. Here we provide the first evidence directly linking rapidly evolved genomic regions to language abilities in contemporary humans. Through extensive analysis of 65 million years of evolutionary events in over 30,000 individuals, we demonstrate that Human Ancestor Quickly Evolved Regions (HAQERs)[5] - sequences that rapidly accumulated mutations after the human-chimpanzee split - specifically influence language but not general cognition. These regions evolved to shape language development by altering binding of Forkhead domain transcription factors, including FOXP2. Strikingly, language-associated HAQER variants show higher prevalence in Neanderthals than modern humans, have been stable throughout recent human history, and show evidence of convergent evolution across other mammalian vocal learners. An unexpected pattern of balancing selection acting on these apparently beneficial alleles is explained by their pleiotropic effects on prenatal brain development contributing to birth complications, reflecting an evolutionary trade-off between language capability and reproductive fitness. By developing the Evolution Stratified-Polygenic Score analysis, we show that language capabilities likely emerged before the human-Neanderthal split - far earlier than previously thought[3, 6, 7]. Our findings establish the first direct link between ancient genomic divergence and present-day variation in language abilities, while revealing how evolutionary constraints continue to shape human cognitive development.

Who, when, where, and why: A systematic review of "late diagnosis" in autism

An autism diagnosis can be a critical milestone toward effective and affirming support. Despite the sharp increase in the number of studies focused on late diagnosis over the last 15 years, there remains no consensus as to what constitutes a late diagnosis of autism, with cutoffs ranging from infancy to middle adulthood. This preregistered systematic review evaluated (a) the field's current quantification of late diagnosis in autism, (b) how the threshold for late diagnosis varies as a function of demographic and population factors, and (c) trends over time. Of the 11,697 records retrieved, N = 420 articles met inclusion criteria and were extracted. Articles spanned 35 years (1989-2024) and included participants from every continent except Antarctica. Only 34.7% of included studies provided a clear threshold for "late diagnosis" (n = 146/420). Late diagnosis cutoffs averaged 11.53 years (range = 2-55 years; median = 6.5 years) with a bimodal distribution (3 and 18 years). The threshold for late diagnosis varied by participant location, F(5,140) = 10.4, p < 0.0001, and sample age, F(5,140) = 20.1, p < 0.0001. Several key rationales for age determinations emerged, including access to services, considerations for adult diagnoses, and data driven approaches. What authors consider to be a "late" diagnosis of autism varies greatly according to research context. Justifications for a specific late-diagnosis age cutoff varied, underscoring the need for authors to contextualize their conceptualizations.

Lingo: an automated, web-based deep phenotyping platform for language ability

Background

Language and the ability to communicate effectively are key factors in mental health and well-being. Despite this critical importance, research on language is limited by the lack of a scalable phenotyping toolkit.

Methods

Here, we describe and showcase Lingo - a flexible online battery of language and nonverbal reasoning skills based on seven widely used tasks (COWAT, picture narration, vocal rhythm entrainment, rapid automatized naming, following directions, sentence repetition, and nonverbal reasoning). The current version of Lingo takes approximately 30 minutes to complete, is entirely open source, and allows for a wide variety of performance metrics to be extracted. We asked > 1,300 individuals from multiple samples to complete Lingo, then investigated the validity and utility of the resulting data.

Results

We conducted an exploratory factor analysis across 14 features derived from the seven assessments, identifying five factors. Four of the five factors showed acceptable test-retest reliability (Pearson's R > 0.7). Factor 2 showed the highest reliability (Pearson's R = 0.95) and loaded primarily on sentence repetition task performance. We validated Lingo with objective measures of language ability by comparing performance to gold-standard assessments: CELF-5 and the VABS-3. Factor 2 was significantly associated with the CELF-5 "core language ability" scale (Pearson's R = 0.77, p-value < 0.05) and the VABS-3 "communication" scale (Pearson's R = 0.74, p-value < 0.05). Factor 2 was positively associated with phenotypic and genetic measures of socieconomic status. Interestingly, we found the parents of children with language impairments had lower Factor 2 scores (p-value < 0.01). Finally, we found Lingo factor scores were significantly predictive of numerous psychiatric and neurodevelopmental conditions.

Conclusions

Together, these analyses support Lingo as a powerful platform for scalable deep phenotyping of language and other cognitive abilities. Additionally, exploratory analyses provide supporting evidence for the heritability of language ability and the complex relationship between mental health and language.